• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.650-652
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• 2022

Frame rate up-conversion (FRUC) algorithm is an image interpolation technology that improves the frame rate of moving pictures. This solves problems such as screen shake or blurry motion caused by low frame rate video in high-definition digital video systems, and provides viewers with a more free and smooth visual experience. In this paper, we propose a video compression technique using deep learning-based FRUC algorithm. The proposed method compresses and transmits after excluding some images from the original video, and uses a deep learning-based interpolation method in the decoding process to restore the excluded images, thereby compressing them with high efficiency. In the experiment, the compression performance was evaluated using the decoded image and the image restored by the FRUC algorithm after encoding the video by skipping 1 or 3 pages. When 1 and 3 sheets were excluded, the average BD-rate decreased by 81.22% and 27.80%. The reason that excluding three images has lower encoding efficiency than excluding one is because the PSNR of the image reconstructed by the FRUC method is low.

• Journal of the Semiconductor & Display Technology
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• v.16 no.2
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• pp.1-8
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• 2017

The visual inspection of electronic parts consists of two steps: automatic visual inspection and verification inspection. In the stage of a verification inspection, the human inspector sequentially inspects all the areas which detected in the automatic inspection. In this study, we propose an algorithm to determine the order of verification inspection by Bayes inference well known in the field of machine learning. This is a method of prioritizing a region estimated to have a high probability of defect using experience data of past inspection. This algorithm was applied to the visual inspection of ultraviolet filters to verify its effectiveness. As a result of the comparison experiment, it was confirmed that the verification inspection can be completed 30% of the conventional method by adapting proposed algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1128-1145
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• 2022

Owing to the rapid development of information science, data analysis based on machine learning has become an interdisciplinary and strategic area. Marine predators algorithm (MPA) is a novel metaheuristic algorithm inspired by the foraging strategies of marine organisms. Considering the randomness of these strategies, an improved algorithm called co-evolutionary cultural mechanism-based marine predators algorithm (CECMPA) is proposed. Through this mechanism, search agents in different spaces can share knowledge and experience to improve the performance of the native algorithm. More specifically, CECMPA has a higher probability of avoiding local optimum and can search the global optimum quickly. In this paper, it is the first to use CECMPA to perform feature subset selection and optimize hyperparameters in support vector machine (SVM) simultaneously. For performance evaluation the proposed method, it is tested on twelve datasets from the university of California Irvine (UCI) repository. Moreover, the coronavirus disease 2019 (COVID-19) can be a real-world application and is spreading in many countries. CECMPA is also applied to a COVID-19 dataset. The experimental results and statistical analysis demonstrate that CECMPA is superior to other compared methods in the literature in terms of several evaluation metrics. The proposed method has strong competitive abilities and promising prospects.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.7
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• pp.720-728
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• 1990

A learning control method is proposed in this paper, using a knowledge base which contains control rules, data, and patterns of the past experience of a plant. The knowledge for plant control is retrieved from measurement data during operation and continually modified after control performance evaluation. A control method is proposed using tinually modified after control performance evaluation. A control method is proposed using fuzzy model of the plant and a recursive statistic decision method of fuzzy subset for control rule generation. Also, the resulting knowledge-based control algorithm has been applied to aprocess and its performance improvement and proper generation of appropriate control rules have been verified.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.327-331
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• 2018

The purpose of this study was to design a competence teaching-learning model that could help college students improve their computational thinking among core competences in SW education. A competence teaching-learning model, UDDPAAP (Unplugged-Demonstration-Decomposition-Pattern Recognition-Abstraction-Algorithm-Programming), was designed by analyzing competences of learners with no experience in software coding, by reconstructing DMM, DDD, and DPAA among the five existing SW-based teaching-learning models, and by analyzing unplugged activity and the Bebras challenge computational thinking scale carefully. The unplugged activity partially adapted to instruction for college students and some items chosen from the Bebras challenge computational thinking scale were applied to the existing teaching-learning model. To determine the effects of the study, pretest was conducted in freshmen for computational thinking and self-confidence on the basis of the experience in SW and computer information literacy education, and posttest following instruction applying the UDDPAAP teaching-learning model. The students provided with SW education based on the UDDPAAP teaching-learning model saw their computational thinking competence improved.

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.1-10
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• 2021

In the past, designers relied primarily on past experience and reference to industry standard thresholds to design spaces. Such design often results in spaces that do not meet the needs of users. The purpose of this paper is to investigate the process and way of generating design parameters by constructing a BP neural network algorithm for spatial design. From the perspective. This paper adopts an experimental research method to take a kindergarten with a large number of complex needs in space as the object of study, and through the BP neural network algorithm in machine learning, the correlation between environmental behavior parameters and spatial design parameters is imprinted. The way of generating spatial design parameters is studied. In the future, the corresponding spatial design parameters can be derived by replacing specific environmental behavior influence factors, which can be applied to a wider range of scenarios and improve the efficiency of designers.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.2
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• pp.357-372
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• 2012

Existing e-learning systems not only require complex admission processes but also do not give stepwise education methods according to individual learners' characteristic. These circumstances cause learners to lose educational interest so that their educational efficiency decreases. In particular, the present e-learning systems do not provide educational approaches suitable for infant and elementary children. Under this system, the e-learning education for children does not proceed completely without guardians. To solve this problem, we design and implement an English e-learning system for elementary children based on friendly and comfortable user interfaces. For children, the proposed system reflects their age and individual interesting per each e-learning stage. This system supports both the Web application platform and smart phone application platform for various client requirements. The proposed system manages 3 classes as English learning content. Learners can experience their own English e-learning course in each class, which is compiled by current educational ability. In addition to the general functions in e-learning system, the proposed system develops content buffering algorithm to reduce data traffic in server.

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.1
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• pp.54-61
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• 1999

FCM(Fuzzy Cognitive Map) is proposed for representing causal reasoning. Its structure allows systematic causal reasoning through a forward inference. By using the FCM, authors have proposed FCM-based fault diagnostic algorithm. However, it can offer multiple interpretations for a single fault. In process engineering, as experience accumulated, some form of quantitative process knowledge is available. If this information can be integrated into the FCM-based fault diagnosis, the diagnostic resolution can be further improved. The purpose of this paper is to propose an enhanced FCM-based fault diagnostic scheme. Firstly, the membership function of fuzzy set theory is used to integrate quantitative knowledge into the FCM-based diagnostic scheme. Secondly, modified TAM recall procedure is proposed. Considering that the integration of quantitative knowledge into FCM-based diagnosis requires a great deal of engineering efforts, thirdly, an automated procedure for fusing the quantitative knowledge into FCM-based diagnosis is proposed by utilizing self-learning feature of neural network. Finally, the proposed diagnostic scheme has been tested by simulation on the two-tank system.

• Journal of IKEEE
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• v.25 no.1
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• pp.25-31
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• 2021

The conventional credit card number recognition system generally needs a card to be placed in a designated location before its processing, which is not an ideal user experience especially for people with visual impairment. To improve the user experience, this paper proposes a novel algorithm that can automatically detect the location of a credit card number based on the fact that a group of sixteen digits has a fixed aspect ratio. The proposed algorithm first performs morphological operations to obtain multiple candidates of the credit card number with >4:1 aspect ratio, then recognizes the card number by testing each candidate via OCR and BIN matching techniques. Implemented with OpenCV and Firebase ML, the proposed scheme achieves 77.75% accuracy in the credit card number recognition task.

• Journal of Korea Multimedia Society
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• v.15 no.8
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• pp.1067-1074
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• 2012

In this paper, we proposed a contents of Chinese characters learning based on gesture recognition using HMM(hidden markov model) algorithm. Input image of the system is obtained in 3-dimensional information from the TOF camera, and the method of gesture recognition is consisted of part of forecasting user's posture in two infrared images and part of recognizing gestures from continuous poses. In the communication between human and computer, this system provided convenience that user can manipulate it easily by not using any further equipment but action. Because this system raise immersion and interest by using two large display and various multimedia factor, it can maximize information transmission. The edutainment Chinese character contents proposed in this paper provide educational effect that use can master Chinese character naturally with interest, and it can be expected a synergy effect via content experience because it is based on gesture recognition.